SOTAVerified

Representation Learning

Representation Learning is a process in machine learning where algorithms extract meaningful patterns from raw data to create representations that are easier to understand and process. These representations can be designed for interpretability, reveal hidden features, or be used for transfer learning. They are valuable across many fundamental machine learning tasks like image classification and retrieval.

Deep neural networks can be considered representation learning models that typically encode information which is projected into a different subspace. These representations are then usually passed on to a linear classifier to, for instance, train a classifier.

Representation learning can be divided into:

  • Supervised representation learning: learning representations on task A using annotated data and used to solve task B
  • Unsupervised representation learning: learning representations on a task in an unsupervised way (label-free data). These are then used to address downstream tasks and reducing the need for annotated data when learning news tasks. Powerful models like GPT and BERT leverage unsupervised representation learning to tackle language tasks.

More recently, self-supervised learning (SSL) is one of the main drivers behind unsupervised representation learning in fields like computer vision and NLP.

Here are some additional readings to go deeper on the task:

( Image credit: Visualizing and Understanding Convolutional Networks )

Papers

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Showing 7551–7600 of 10580 papers

TitleStatusHype
Disentangled Representation with Dual-stage Feature Learning for Face Anti-spoofing—0
Causal Effect Estimation: Recent Advances, Challenges, and Opportunities—0
Synthetic Data Can Also Teach: Synthesizing Effective Data for Unsupervised Visual Representation Learning—0
Disentangled Representation with Causal Constraints for Counterfactual Fairness—0
Learnability for the Information Bottleneck—0
Causal disentanglement of multimodal data—0
Arctic-Embed 2.0: Multilingual Retrieval Without Compromise—0
Quality-Aware Prototype Memory for Face Representation Learning—0
Disentangled Representations in Neural Models—0
LCOT: Linear circular optimal transport—0
Disentangled Representation Learning with Transmitted Information Bottleneck—0
Quantifying Error in the Presence of Confounders for Causal Inference—0
LayoutMask: Enhance Text-Layout Interaction in Multi-modal Pre-training for Document Understanding—0
Layer-Wise Evolution of Representations in Fine-Tuned Transformers: Insights from Sparse AutoEncoders—0
Quantum Architecture Search with Unsupervised Representation Learning—0
Disentangled Representation Learning with Large Language Models for Text-Attributed Graphs—0
Quaternion-Based Graph Convolution Network for Recommendation—0
Quaternion Collaborative Filtering for Recommendation—0
Disentangled Representation Learning with Wasserstein Total Correlation—0
Arch-LLM: Taming LLMs for Neural Architecture Generation via Unsupervised Discrete Representation Learning—0
Adversarial Stacked Auto-Encoders for Fair Representation Learning—0
A CTC Triggered Siamese Network with Spatial-Temporal Dropout for Speech Recognition—0
A bi-diffusion based layer-wise sampling method for deep learning in large graphs—0
Layerwise Bregman Representation Learning with Applications to Knowledge Distillation—0
Layer-stacked Attention for Heterogeneous Network Embedding—0
Disentangled Representation Learning with Sequential Residual Variational Autoencoder—0
LAWDR: Language-Agnostic Weighted Document Representations from Pre-trained Models—0
q-VAE for Disentangled Representation Learning and Latent Dynamical Systems—0
Disentangled Representation Learning with Information Maximizing Autoencoder—0
LaundroGraph: Self-Supervised Graph Representation Learning for Anti-Money Laundering—0
Lattice Representation Learning—0
Disentangled Representation Learning Using (β-)VAE and GAN—0
Lattice Representation Learning—0
RandAlign: A Parameter-Free Method for Regularizing Graph Convolutional Networks—0
Disentangled Representation Learning with the Gromov-Monge Gap—0
Causal Climate Emulation with Bayesian Filtering—0
LaT: Latent Translation with Cycle-Consistency for Video-Text Retrieval—0
Random Forest Autoencoders for Guided Representation Learning—0
LaTiM: Longitudinal representation learning in continuous-time models to predict disease progression—0
Random vector functional link neural network based ensemble deep learning for short-term load forecasting—0
Latent Space Translation via Inverse Relative Projection—0
Latent Space Conditioning on Generative Adversarial Networks—0
Disentangled Representation Learning GAN for Pose-Invariant Face Recognition—0
Causal-Based Supervision of Attention in Graph Neural Network: A Better and Simpler Choice towards Powerful Attention—0
Latent Space Characterization of Autoencoder Variants—0
LatentSLAM: unsupervised multi-sensor representation learning for localization and mapping—0
Disentangled Representation Learning for Causal Inference with Instruments—0
Ranking Loss and Sequestering Learning for Reducing Image Search Bias in Histopathology—0
Latent Representation Learning for Multimodal Brain Activity Translation—0
Disentangled Representation Learning for Parametric Partial Differential Equations—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SciNCLAvg.81.8—Unverified
2SPECTERAvg.80—Unverified
3CiteomaticAvg.76—Unverified
4Sci-DeCLUTRAvg.66.6—Unverified
5SciBERTAvg.59.6—Unverified
6BioBERTAvg.58.8—Unverified
7CiteBERTAvg.58.8—Unverified
#ModelMetricClaimedVerifiedStatus
1top_model_weights_with_3d_21:1 Accuracy0.75—Unverified
#ModelMetricClaimedVerifiedStatus
1Resnet 18Accuracy (%)97.05—Unverified
#ModelMetricClaimedVerifiedStatus
1Morphological NetworkAccuracy97.3—Unverified
#ModelMetricClaimedVerifiedStatus
1Max Margin ContrastiveSilhouette Score0.56—Unverified